Earticle

초록

영어

Predicting subcellular localizations of human proteins become crucial, when new unknown proteins sequences do not have significant homology to proteins of known subcellular locations. In this paper, we present a novel approach to develop CE-Hum-PLoc system. Individual classifiers are created by selecting a fixed learning algorithm from a pool of base learners and then trained by varying feature dimensions of Amphiphilic Pseudo Amino Acid Composition. The output of combined ensemble is obtained by fusing the predictions of individual classifiers. Our approach is based on the utilization of diversity in feature and decision spaces. As a demonstration, the predictive performance was evaluated for a benchmark dataset of 12 human proteins subcellular locations. The overall accuracies reach upto 80.83% and 86.69% in jackknife and independent dataset tests, respectively. Our method has given an improved prediction as compared to existing methods for this dataset. Our CEHum-PLoc system can also be a used as a useful tool for prediction of other subcellular locations.

목차

Abstract
 1. Introduction
 2. Materials and Methods
  2.1. Proposed method
  2.2. Evaluation methods
 3. Results and Discussions
 4. Conclusion
 References

키워드

저자

• Abdul Majid [ Department of Mechatronics, Gwangju Institue of Science and Technology, Department of Information and Computer Sciences, Pakistan Institute of Engineering and Applied Sciences ]
• Tae-Sun Choi [ Department of Mechatronics, Gwangju Institue of Science and Technology ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605